Predictive neurodiagnostic methods

ABSTRACT

Disclosed are methods of predicting the risk of developing a neurological disorder in a mammalian subject and methods of use of this profile for providing neuroanalytical services for an end user. Also disclosed is a system comprising a processor and a memory having a neurodiagnostic algorithm and plurality of data sets, the processor being capable of reading the data sets, executing the neurodiagnostic algorithm, and deriving a risk profile therefrom.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/016,302 entitled “Predictive NeurodiagnosticMethods,” which was filed Jun. 24, 2014, U.S. Provisional PatentApplication Ser. No. 62/016,309 entitled “Neuroanalytical Services,”which was filed Jun. 24, 2014, U.S. Provisional Patent Application Ser.No. 62/016,315 entitled Neurodiagnostic Analysis System,” filed on Jun.24, 2014, U.S. Provisional Patent Application Ser. No. 62/094,214entitled “Predictive Neurodiagnostic Methods,” which was filed Dec. 19,2014, U.S. Provisional Patent Application Ser. No. 62/094,219 entitled“Neuroanalytical Services,” which was filed Dec. 19, 2014, and U.S.Provisional Patent Application Ser. No. 62/094,223 entitled“Neurodiagnostic Analysis System,” filed on Dec. 19, 2014. The entiretyof the aforementioned applications are herein incorporated by reference.

FIELD OF THE INVENTION

The present disclosure relates to systems and methods for evaluatingneurological disorders, and to databases and computational systems forpredicting disease and treatment outcomes.

BACKGROUND

According to multiple patent advocacy groups around the globe, theprevalence of many neurological disorders, including those affecting theaging population in the developed countries of the world, is estimatedat over 20 million people today and is expected to quadruple in the nexttwenty years. Research and development of treatments by pharmaceuticaland biotech companies for these disorders has lagged behind diseaseprognosis. Clinical trials for the few new therapeutic drugs requirehuge financial expenditures by these companies, and often they failbecause they are looking for drugs that are for “one size fits all”patients.

Furthermore, the presently available therapeutics may only betemporarily or mildly efficacious, as the patient group for which thedrug was developed was different than the present patient in need oftreatment. In some cases, presently available therapeutics are notefficacious at all because the damage done to the CNS before diagnosisof the disorder and subsequent treatment may be irreversible.

Providing treatment to a patient at an earlier stage of the disease, orbetter yet, before the disease presents itself in an asymptomaticpatient and causes irreparable damage to the nervous system of thepatient, would be beneficial.

Data on patients with neurological disorder do exist, as it is collectedby hospitals, universities, disease foundations, and pharmaceuticalcompanies undertaking preclinical and clinical trials. In addition, theincidence of patients self-initiating testing such as DNA and biomarkeranalysis is increasing. However, all of this existing data is notcentrally located, in many cases is in too small a sample size and isthus not statistically and/or clinically relevant, and often is obtainedwith selection biases and over a limited time frame. Thus, comprehensivedata on patient populations does not exist, and this data is needed todesign targeted drugs and diagnostics against specific diseases andpopulations of patients.

Thus, what is needed are comprehensive, controlled databases ofneurological information on single patients and on worldwide populationsof patients and controls. What is also needed for drug and diagnosticdevelopment are global databases of neurological information fromspecific, qualified clinical trial populations.

SUMMARY

In one aspect, the disclosure provides a method of predicting the riskof developing a neurological disorder in a first mammalian subject. Thesubject may be asymptomatic or symptomatic. The method comprises thesteps of: screening for the presence of one or more biomarkers;performing diagnostic imaging of the subject; performing behavioraltests indicative of the neurological disorder; measuring the subject'sexposure to an environmental factor; measuring/identifying a physicalcharacteristic of the subject; determining the presence of theneurological disorder in a family member of the first subject; combiningthe results from the steps above; and comparing the combined resultswith combined results obtained from a second mammalian subject diagnosedwith the neurological disorder, a high correlation between the combinedresults from the second subject and the combined results obtained fromthe first subject being indicative of a heightened risk of the firstsubject developing the neurological disorder.

As used herein, a “high” or “heightened correlation” refers to acorrelation coefficient of at least 0.7. Correlations can range from 0(no correlation) to 1 (maximum correlation). When two variables have acorrelation of 1, it means that the value of one variable can bepredicted by the value of the other variable. Likewise, a lowcorrelation means the 2 variable values have little to do with eachother.

In some embodiments, the screening step comprises screening for abiomarker which is a nucleic acid, polypeptide, prion, virus, brainplaque, CNS plaque, fibril, intranuclear neuronal inclusions, and/orbrain structure abnormality. In certain embodiments, the biomarker is anucleic acid such as a gene, or coding portion thereof, a SNP, an mRNA,a miRNA, a pri-miRNA, or a prepri-miRNA. In particular embodiments,nucleic acid biomarker is over-expressed miR-196a, miR-29a, or miR-330.In other particular embodiments, the nucleic acid biomarker isunder-expressed miR-133b, miR-205, miR-34b/c, miR-9, miR-9*, or miR-132.In different embodiments, the nucleic acid biomarker is a mutated Cu/Znsuperoxide dismutase 1 (SOD1) gene, an unstable microsatellite repeat(insertion mutation) in a gene, a mutated HIT gene, androgen receptorgene (on the X chromosome), ATXN1, ATXN2, ATXN3, ATXN7, TBP, CACNA1A,C9orf72 (on chromosome 9), FMR1 (on the X-chromosome), AFF2 (on theX-chromosome), FMR2 (on the X-chromosome), FXN or X25, (frataxin—reducedexpression), DMPK, OSCA or SCA8, PPP2R2B or SCA12, α-synuclein,glucocerebrosidase (GBA), ABHD12, SNCA, or LRRK2. or a leucine-richrepeat kinase 2 (LRRK-2) gene. In yet other embodiments, the biomarkeris a polypeptide which is a surface marker, tau protein, beta amyloid,polyglutamate (peptide), alpha-synuclein, non-Abeta component (NAC),polyQ expansion, TDP-43 protein aggregate, FUS protein aggregate, ormutant Huntingtin aggregate. In some embodiments, the biomarker anaberrant structure such as is a Lewy body fibril, neurofibrillarytangle, or alpha-synuclein fibril, or is an amyloid plaque or a senileplaque. In still other embodiments, the biomarker is a virus such asHerpes simplex virus-1(HSV-1 type HHV-1), roseolovirus (type HHV-6),Epstein Barr virus (EBV type HHV-4), Varicella zoster virus (VZV typeHHV3), H1N1 Influenza a viruses, HIV, and/or HTLV-II, or a particle of avirus.

In some embodiments, the screening step is performed by obtaining asample of a body fluid or tissue and screening for the biomarker in thesample. In certain embodiments, the sample is obtained from thesubject's blood, cerebral spinal fluid, serum, lymph, saliva, lacrimalsecretion, sweat, mucous, vaginal secretion, lymph, urine, or seminalfluid.

In certain embodiments, the diagnostic imaging step of the methodperformed obtaining an x-ray, a computerized axial tomographic (CAT)scan, magnetic resonance imaging (MRI) scan, functional MRI (fMRI) forblood-oxygen-level-dependent (BOLD) imaging, single photon emissioncomputed tomography (SPECT) perfusion image, computed tomography (CT)scan, proton MR spectroscopy scan, positron emission tomographic (PET)scan, and/or [F-18] fluoro-2-deoxy-D-glucose-positron emissiontomographic (18F-FDG PET) scan, and/or ultrasound scan. In certainembodiments, these scans employ radio-labeled imaging reagents whichtarget specific receptors on neurons or proteins in the brain. Inspecific embodiments, the imaging reagent is DaTScan, Amyvid, or similarreagents.

In some embodiments, the behavioral test performed measures sensoryabilities, motor functions, body weight, body temperature, and/or painthreshold, learning abilities, memory, and symptoms of anxiety,depression, schizophrenia, and/or drug addiction.

In certain embodiments, the behavioral test performed measures acousticstartle, eye blink, pupil constriction, visual cliff, auditorythreshold, and/or olfactory acuity.

In some embodiments, wherein the subject's exposure to pesticides,herbicides, fungicides, solvents, other toxic chemicals, tobacco smoke,heavy metals, electromagnetic fields, ultraviolet radiation, and/or diet(malnutrition, vitamin deficiency), and/or alcohol consumption ismeasured. In particular embodiments, the subject's exposure to MPTP(1-methyl-4-phenyl-1,2,3,6-tetrahydro-pyridine), rotenone, paraquat,maneb, Agent orange, manganese, lead, iron, methylmercury, copper, zinc,selenium, polychlorinated biphenyls, and/or a reactive oxygen species(ROS) is measured. In certain embodiments, exposure of the subject tothe environmental factor causes apoptosis, oxidative stress, perturbedcalcium homeostasis (loss of intracellular Ca⁺²), excitotoxicity,mitochondrial dysfunction, and/or activation of caspases.

In other embodiments, the physical factor measured is the age, gender,ethnicity, heart rate, REM, electrical signals from the heart or brain,and/or the presence of genetic polymorphisms, endocrine conditions,oxidative stress, inflammation, stroke, traumatic brain injury,hypertension, diabetes, head/CNS trauma, depression, infection, cancer,vitamin deficiency, and/or immune and/or metabolic condition of thesubject.

The present method is predictive of a neurological disorder such asneurodegenerative, neurotrauma, or neuropsychology disorders.

In some embodiments, the neurological disorder is a neurodegenerativedisorder such as a polyglutamine (PolyQ) disease or a non-polyglutaminedisease. In certain embodiments, the polyglutamine disease isSpinocerebellar ataxia type 1 (SCA1), SCA2 (Spinocerebellar ataxia Type2), SCA3 (Spinocerebellar ataxia Type 3 or Machado-Joseph disease), SCA6(Spinocerebellar ataxia Type 6), SCAT (Spinocerebellar ataxia Type 7),SCA17 (Spinocerebellar ataxia Type 17), DRPLA(Dentatorubropallidoluysian atrophy), HD (Huntington's disease), SBMA(Spinobulbar muscular atrophy or Kennedy disease), dentatorubralatrophy, or pallidoluysian atrophy. In other embodiments, thenon-polyglutamine disease is FRAXA (Fragile X syndrome), FXTAS (FragileX-associated tremor/ataxia syndrome), FRAXE (Fragile XE mentalretardation), FRDA (Friedreich's ataxia), DM (Myotonic dystrophy), SCA8(Spinocerebellar ataxia Type 8), or SCA12 (Spinocerebellar ataxia Type12.

In other embodiments, the neurological disorder is a neurotraumadisorder resulting from a traumatic brain injury or concussion, or froma stroke.

In yet other embodiments, the neurological disorder is a neuropsychologydisorder such as autism, ADHD, anxiety, depression, bipolar disorder,dyslexia, epilepsy, obsessive compulsive disorder, schizophrenia, orsocial phobia. It yet other embodiments, the neuropsychology disorder isArachnoid cysts, Arachnoiditis, Asperger's Syndrome, AtaxiaTelangiectasia, Arteriovenous Malformations, AttentionDeficit/Hyperactivity Disorder, Autism Barth Syndrome, Batten Disease,Behcet's Disease, Bell's Palsy Bernhardt-Roth Syndrome, Binswanger'sDisease, Blepharospasm Bloch-Sulzberger Syndrome, Brown-SequardSyndrome, CADASIL Canavan Disease, Capgras Syndrome, Causalgia CentralCord Syndrome, Central Pain Syndrome, Central Pontine Myelinolysis,Cerebellar Hypoplasia, Cerebral Anoxia/Hypoxia, CerebralArteriosclerosis, Cerebral Cavernous Malformations, Cerebral Palsy,Cerebro-Oculo-Facio-Skeletal Syndrome, Charcot-Marie-Tooth Disease,Chiari Malformations, Childhood Anxiety Disorders, ChildhoodDisintegrative Disorder, Childhood Mood Disorders, Cholesteryl-EsterStorage Disease, Chronic Inflammatory Demyelinating Polyneuropathy,Chronic Pain Syndrome, Chung-Strauss Syndrome, Cluster HeadachesCoffin-Lowry Syndrome, Colpocephaly Coma & Persisting Vegetative StateConduct Disorder, Oppositional Defiant Disorder, Congenital MyastheniaCongenital Myopathy, Corticobasal Degeneration, Craniosynostosis,Creutzfeld-Jakob Disease, Cushing's Disease, CVAs, CytomegalovirusDandy-Walker Syndrome, Dawson Disease, De Morsier's Syndrome,Dejerne-Klumpke Palsy Delirium, Dementia Pugilistica, Dermatomyositis,Device's Disease, Diabetic Neuropathy, Disconnect Syndromes, Disordersof Written Language, Down Syndrome, Dravet Syndrome, Dysautonomia,Dyssynergia, Cerebellaris Myoclonica Dystonias, Empty Sella Syndrome,Encephalitis, Encephalopathy, Encephalo-celes, Epilepsy, Erb-DuchennePalsy, Fabry Disease, Fahr's Syndrome, Familial Periodic Paralyses,Familial Spastic Paraplegia, Farber's Disease, Fatal Familial Insomnia,Febrile Seizures, Fibromuscular Dysplasia, Fibromyalgia, Fragile XSyndrome, Friedeich's Ataxia, Frontotemporal Dementia, Gaucher Disease,Gerstmann-Straussler-Scheinker Disease, Gerstmann Syndrome,Glossopharyngeal neuralgia, Guillain-Barre, Hallervorden-Spatz Disease,Hemicrania, Continua Hemifacial Spasm, Hereditary Spastic Paraplegia,Herpes Zoster Oticus, HIV/AIDS, HIV/AIDS Dementia Complex, Holmes-AdieSyndrome, Holoprosencephaly, Homocystinurua, Hughes Syndrome,Huntington's Disease, Hydramyelia, Hydranencephaly, Hydrocephalus,Hydromyelia, Hypersomnia, Hypertonia, Hypotonia Increased IntracranialPressure, Infantile Hypotonia, Infantile Neuroaxonal Dystrophy,Infantile Refsum Disease, Infantile Spasms/West Syndrome, Iniencephaly,Intrauterine Teratogen Exposure, Isaac's Syndrome, Joubert Syndrome,Kawasaki Disease, Kearns-Sayre Syndrome, Kennedy's Disease, KinsbourneSyndrome, Kleine-Levin Syndrome, Klinefelter Syndrome, Klippel-FeilSyndrome, Klippel-Trenaunay Syndrome, Kluver-Bucy Syndrome, KrabbeDisease, Kuru Lambert-Eaton Myasthenia Syndrome, Landau-KleffnerSyndrome, Lead Poisoning Leigh's Disease, Lennox-Gastaut Syndrome,Lesch-Nyhan Syndrome, Lewy-Body Dementia, Lipoid ProteinosisLissencephaly, Locked-in Syndrome, Lyme Disease, Machado-Joseph Disease,Macrencephaly, Maple Syrup Urine Disease, Mathematics Disorders MeakesDisease, Meningitis, Microcephaly, Migraine, Mitochondrialcardiomyopathies, Mitochondrial Myopathies, Megalencephaly,Melkersson-Rosenthal Syndrome, Mental Retardation, MetachromaticLeukodystrophy, Miller-Fisher Syndromes, Mobius Syndrome, MonomelicAmyotrophy, Motor Neuron Diseases, Moyamoya Disease,Mucopolysaccharidosis, Multifocal Motor Neuropathy, Multi-InfarctDementia. Multiple Sclerosis, Multi-System Atrophy with OrthostaticHypotension, Multi-System Atrophy without Orthostatic Hypotension,Muscular Dystrophy, Myasthenia Gravis, Myoclonus Myopathy, Myotonia,congenital Narcolepsy, Neuroacanthocytosis, Neurofibromatosis.Neuroleptic Malignant Syndrome, Neuronal Ceroid Lipofuscinoses,Neurosarcoidosis, Neurosyphilis, Neurotoxicity, Niemann-Pick Disease,Nonverbal Learning Disability, Normal Pressure Hydrocephalus OccipitalNeuralgia, Ohtahara Syndrome, Olivopontocerebellar Atrophy, OpsoclonusMyoclonus, Orthostatic Hypotension, Paraneolastic Syndromes,Parasthesias, Parkinson's Disease, Paroxysmal Choreoathetosis,Paroxysmal Hemicrania, Parry-Romberg, Pelizaeus-Merzbacher Disease,Periarteritis Nodosa, Peripheral Neuropathy, PeriventricularLeukomalacia, Pick's Disease, Piriformis Syndrome, PKU PolymyositisPompe Disease, Porencephaly, Postural Tachycardia, Prader Willi, PrimaryLateral Sclerosis, Primary Progressive Aphasia, Progressive MultifocalLeukoencephalopathy, Progressive Supranuclear Palsy, PseudotumorCerebri, Psychotic Disorders, Rasmussen's Encephalitis, ReadingDisorders, Repetitive Motor Disorders, Restless Leg Syndrome, RettSyndrome, Reye's Syndrome, Rheumatoid Arthritis, Sandhoff Disease,Schilder's Disease, Schizencephaly, Sclerodoma, Semantic Dementia,Septo-Optic Dysplasia, Shaken Baby Syndrome, Shingles, Sjogren'sSyndrome, Sleep Apnea, Somatoform and Conversion Disorders, SotosSyndrome, Spina Bifida, Spinal Cord Injuries, Spinal Muscular Atrophy,Spinocerebellar degeneration, Stiff-Person Syndrome, StriatonigralDegeneration, Sturge-Weber Syndrome, Subcortical-Vascular Dementia,SUNCT Headaches, Sydenham Chorea, Syncope Syringo-myelia, SystemicLupus, Tabes Dorsalis, Takayasu's Disease, Tardive Dyskinesia, TarlovCysts, Tay-Sachs Disease, Tethered Spinal Cord Syndrome, TIAs ThoracicOutlet Syndrome, Thyrotosic Myopathy, Todd's Paralysis, Tourette'sDisorder, other Tic Disorders, Transverse Myelitis, Traumatic BrainInjury, Trigeminal Neuralgia, Tropical Spastic Paraparesis, TroyerSyndrome, Tuberous Sclerosis, Turner Syndrome, Vasculitis, von Economo'sDisease, von Hippel-Lindau Disease, Wallenberg Syndrome, Wegener'sgranulonoatosis, Wernicke-Korsakoff Syndrome, Whiplash, Whipple'sDisease, Williams Syndrome, Wilson's Disease, Wolman's Disease, and/orZellweger Syndrome.

In yet other embodiments, the neurological disorder is Alzheimer'sdisease, multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS),spinocerebellar ataxias, trinucleotide repeat disorder, dementia,multiple system atrophy, HIV-associated neurocognitive disorders (HAND),or polyneuropathy, hearing loss, ataxia, retinitis pigmentosa, andcataract (PHARC), Parkinson's disease, essential tremor, cerebellartremor, dystonic tremor, orthostatic tremor, Parkinsonian tremor, rubraltremor, or psychogenic tremor.

In certain embodiments, the combination steps comprise generating a riskscore, and wherein if the risk score of the first subject is similar tothe risk score of the second subject, the first subject has a heightenedrisk of developing the neurological disorder.

In some other embodiments, the method further comprises developing andimplementing a treatment plan to the first subject, the treatment plancomprising administering a therapeutically effective composition to thefirst subject.

In another aspect, the disclosure provides a system comprising aprocessor and a memory. The memory has a neurodiagnostic algorithm and aplurality of data sets, the data sets including, biomarker screeningdata, diagnostic imaging data, behavioral test data, exposure toenvironmental risk factors, subject health data, family medical historydata. The processor of this system is capable of reading the data sets,executing the neurodiagnostic algorithm or algorithms, and deriving arisk profile therefrom.

In some embodiments, the system further comprises a display means forvisualizing the risk profile. In certain embodiments, the system memoryis at locations distant from the processor.

In another aspect, the disclosure provides data subscription serviceaccessing the data sets of the system, wherein the neurodiagnosticalgorithm predicts treatment outcomes from the risk profile. In oneembodiment, the subject health data is obtained from an individualasymptomatic for neurological disorders.

In another aspect, the disclosure provides a relational databasecomprising a plurality of subject-independent neurodiagnostic data sets.These data sets comprise biomarker screening data, diagnostic imagingdata, behavioral test data, environmental risk factor data, and the oneor more subject-dependent neurodiagnostic data sets further includingsubject medical history and subject family medical histories.

In another aspect, the disclosure provides a data subscription servicefor accessing the relational database.

The present disclosure also provides a method of providingneuroanalytical services. The method comprises generating a patientprofile for neurological risk, whereby generating further comprisesanalyzing subject-independent neuroanalytical data sets andsubject-dependent neuroanalytical data sets; and delivering the patientrisk profile to an end user.

In some embodiments, subject-independent neuroanalytical data setsinclude third-party data of neurological disease-relevant biomarkers,neural imaging data, environmental risk factors for neurologicaldisorders. Subject-dependent neuroanalytical data sets include subjectand family medical, environmental exposure, and behavioral data medicalhistory data.

In certain embodiments, the patient risk profile comprises an aggregatedrisk of individual risk factors delineated or calculated from thesubject-independent neuroanalytical data sets and subject-dependentneuroanalytical data sets.

In some embodiments, an algorithm is biased to value risk higher fromthe subject-dependent neuroanalytical data as compared to risk fromsubject-independent neuroanalytical data.

In some embodiments, the risk profile is delivered to an end user usingSaaS, PaaS, or IaaS-based service models. In some embodiments, hedelivery is real-time or near real-time. In certain embodiments, the enduser obtains the risk profile on a tablet, smartphone or portablecomputing device.

In some embodiments, Private and Public Cloud architecture is employed.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects of the present disclosure, the variousfeatures thereof, as well as the disclosure itself may be more fullyunderstood from the following description, when read together with theaccompanying drawings in which:

FIG. 1 is a diagrammatic representation of how the data can support adiagnosis of a neurological disorder, and select a useful population ofsubjects for a clinical trial for a therapeutic.

DESCRIPTION

The issued U.S. patents, allowed applications, published foreignapplications, and references that are cited herein are herebyincorporated by reference in their entirety to the same extent as ifeach was specifically and individually indicated to be incorporated byreference. Patent and scientific literature referred to hereinestablishes knowledge that is available to those of skill in the art.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs.

The present disclosure provides methods of predicting the risk of asubject for developing a certain neurological disorder using informationderived from data sets obtained from a mammalian subject or groups ofsubjects. Such predictive methods are also useful for following thedevelopment of a neurological disorder or disease, and for selecting anappropriate subject pool for conducting a clinical trial on thetherapeutic efficacy of a novel drug or diagnostic.

A. Sources of Data: How/What Data is Obtained

Information about a single subject or multiple subjects, are obtainedfrom a data source or number of sources. These data sets are thenrelated to the health and physical state of the subject.

For example, one such data source is the results obtained from screeningassays for biomarkers, the presence or absence of which is indicative ofthe presence or predisposition for, a neurological disease or disorder.The biomarker can be any physically present substance, trait, orcharacteristic found in or on the subject. For example, the biomarkercan be a nucleic acid, protein, polypeptide, peptide, prion, virus,brain plaque, CNS plaque, fibril, intranuclear neuronal inclusions,and/or brain structure abnormality.

Some useful biomarkers include a nucleic acid which is a gene or portionthereof, such as a coding portion, a single nucleotide polymorphism(SNP), an mRNA, microRNA (miRNA), a primary transcript of a miRNA(pri-miRNA), or a prepri-miRNA. For example, some useful nucleic acidsinclude an over-expressed miRNA, such as miR-196a, miR-29a, or miR-330,or an under-expressed miRNA, such as miR-133b, miR-205, miR-34b/c,miR-9, miR-9*, or miR-132. Other useful nucleic acid biomarkers aremutant nucleic acids, such as a mutation in the Cu/Zn superoxidedismutase 1 (SOD1) gene, an unstable microsatellite repeat (insertionmutation) in a gene, HTT gene, androgen receptor on the X chromosome,ATXN1, ATXN2, ATXN3, ATXN7, TBP, CACNA1A, mutation in C9orf72 (onchromosome 9), FMR1 (on the X-chromosome), AFF2 (on the X-chromosome),FMR2 (on the X-chromosome), FXN or X25, (frataxin-reduced expression),DMPK, OSCA or SCA8, PPP2R2B or SCA12, α-synuclein, leucine-rich repeatkinase 2 (LRRK-2), glucocerebrosidase (GBA), ABHD12, SNCA, or LRRK2.

Other types of useful biomarkers include proteins, polypeptides, andpeptides such as, but not limited to, a surface marker, tau protein,beta amyloid, polyglutamate (peptide), alpha-synuclein, non-Abetacomponent (NAC), polyQ expansion, TDP-43 protein aggregate, FUS proteinaggregate, or a mutant Huntingtin aggregate. Peptides, polypeptides, andproteins can be detected using one of the many methods know to thosewith skill in the art (see, e.g., Meth. Mol. Biol. (2009) 536:588;Kurien et al. (eds.) Humana Press). The biomarker may also be a prion.Those with skill in the art are aware of methods of detecting prions(see, e.g., Atarashi et al. (2008) Nat. Meth. 3:2011-2012). Thebiomarker may be a Lewy body fibril, a neurofibrillary tangle, or analpha-synuclein fibril amyloid plaque, or a senile plaque, found in theCNS in general or in the brain. Methods of detecting brain plaques areknown to those with skill in the art (see, e.g., Kepe et al. (2006)Meth. Enzymol. 412:144-60), as are methods of detecting neurofibrillarytangles (see, e.g., Murphy et al. (1996) Am. J. Pathol. 149(6):1839-46).

The biomarker may also or alternatively be a virus such as, but notlimited to Herpes simplex virus-1(HSV-1 type HHV-1), roseolovirus (typeHHV-6), Epstein Barr virus (EBV type HHV-4), Varicella zoster virus (VZVtype HHV3), H1N1 Influenza viruses, HIV, or HTLV-I. Methods fordetecting and characterizing virus and viral particles are well known tothose with skill in the art (see, e.g., Mol. Meth. Virus Detect. (1995)(Wiedbrauk and Farkas, eds.) ISBN: 978-0-12-748920-9).

Certain physical characteristics of the subject provide another sourceof data. Such characteristics include age, body temp, heartbeat, pulse,sex, ethnicity, body weight, REM, electrical signals from the heart,body mass index (BMI), and height. Other physical characteristicsinclude the constitution of body fluids, such as breath, blood, plasma,lymph, saliva, seminal fluid, urine, vaginal secretions, lacrimalsecretions, mucous, sweat, and/or mammary secretions. Methods ofanalyzing the chemical makeup of body fluids is well known in the art(see, e.g., Frascione et al. (2012) Analyst 21; 137 (2):508-12; Hu, etal. (2006) Proteomics 6 (23):6326-6353). Other physical factors whichcan be measured include the presence of certain genetic polymorphisms(Shi et al. (1999) Mol. Diag. 4 (4):343-51), endocrine conditions (Choet al. (2010) J. Microbiol. Biotechnol. 20 (11):1563-70), oxidativestress (“Oxidative Stress and Nanotechnology, Methods and Protocols”Meth. Mol. Biol. (2013) 1028, (Armstrong et al., eds.) Humana Press),inflammation (Fischman et al. (1988) Sem. Nucl. Med. 18 (4):335-344),stroke (Kloska et al. (2004) Neuroradiol. 233 (1), traumatic braininjury (Vespa et al. (1999) J. Neurosurg. 91 (5):750-760), hypertension(Pickering (1994) Lancet 344 (8914):31-35), diabetes (C. M. Bennett, etal. (2007) Diabetic Med. 24 (4):333-343), head/CNS trauma (Orrison etal. (1994) AJNR 15:351-356), depression (Garland et al. (2002) BCMJ. 44(9):469-472), infection (Ou et al. (1988) Sci. 239(4837):295-297),cancer (Ferrari (2005) Nature Rev. (2005) 5:161-171), vitamin deficiency(Bates (1999) Oxford J., Br. Med. Bull. 55 (3):643-657), and/or immuneand/or metabolic conditions (Theofilopoulos et al. (1976) J. Clin.Invest. 57 (1):169-182.; Oh et al. (2000) Met. Eng. 2:201-209). Otherphysical factors which can be measured include the presence of certaingenetic polymorphisms, endocrine conditions, oxidative stress,inflammation, stroke, traumatic brain injury, hypertension, diabetes,head/CNS trauma, depression, infection, cancer, vitamin deficiency,and/or immune and/or metabolic conditions.

Another data source is related to information about various internalorgans or systems within the subject. Such data is obtainable usingvarious imaging methodologies. For example, depending on what type ofinformation is being sought, the diagnostic imaging can be performed bytaking an x-ray, a CAT scan, MRI, fMRI for BOLD imaging, SPECT perfusionimage, CT scan, proton MR spectroscopy scan, PET scan, and/or 18F-FDGPET scan, and/or ultrasound. These scans can employ radio-labeledimaging reagents such as DaTScan, Amyvid, and similar reagents, whichtarget specific receptors on neurons or proteins in the brain.

Yet another source of data is information obtained by performingbehavioral tests indicative of the presence of, or predisposition for,various neurological disorders. Results of behavioral tests performed bythe subject can be indicative of the predisposition, presence, or levelof severity, of the neurological disorder. Such behavioral test can beany one known in the art, or later developed, which measures sensoryabilities, such as acoustic startle, eye blink, pupil constriction,visual cliff, auditory threshold, and olfactory acuity. Usefulbehavioral tests also include those that measure motor functions,pain/pressure/temperature threshold(s), learning abilities, memory, andthose which measure behavioral symptoms of anxiety, depression,schizophrenia, and/or drug addiction. Cognitive tests which assess thecognitive capabilities of humans and other animals, such as IQ tests,mirror tests (a test of visual self-awareness), and the T maze test(which tests learning ability) are useful as well. One with skill in theart is aware of many of such behavioral tests (see, e.g. (Benson (1993)Neurol. Clin. 11 (1):1-8.; Farah et al. (1996) Behavioral Neurology andNeuropsychology (McGraw-Hill Profess. Pub.) 1st ed.; Valenstein et al.(2003) Clin. Neuropsychol. (4th ed.) Oxford Univ. Press; Lione et al.(1999) J. Neurosci. 19 (23):10428-10437).

Still another data source is from information about the subject'sexposure to certain environmental factors. For example, the subject'sexposure to pesticides, herbicides, fungicides, solvents, other toxicchemicals, tobacco or marijuana smoke, heavy metals, electromagneticfields, ultraviolet radiation, and/or diet (malnutrition, vitamindeficiency), and/or alcohol consumption is measured. In particularexamples, exposure to MPTP(1-methyl-4-phenyl-1,2,3,6-tetrahydro-pyridine), rotenone, paraquat,maneb, Agent Orange, manganese, lead, iron, methylmercury, mercury,copper, zinc, selenium, polychlorinated biphenyls, and/or a reactiveoxygen species (ROS)(such as oxygen ions and peroxides) is measured.Methods for detecting exposure to various environmental toxins are knownto those with skill in the art (see, e.g., The Fourth National Report onHuman Exposure to Environ. Chem. (2009) CDCP; Wang et al. (2009) NanoLett. 9 (12):4147-4152; Asphahani et al. (2007) Analyst 132(9):835-841.) In some cases, exposure to the environmental factormeasured causes apoptosis, oxidative stress, perturbed calciumhomeostasis (loss of intracellular Ca⁺²), excitotoxicity, mitochondrialdysfunction, and/or activation of caspases in the subject.

Another data source is the family history of the subject for theneurological and/or other disorders. Factors such as the prevalence ofthe disorder in the close versus extended family, at what age thedisorder presented in these family members, the severity of thedisorder, and its ability to be successfully treated in a close relativeor extended family member are useful data.

The combination and manipulation of this data provides a wealth ofinformation, such as the risk of developing a particular neurologicaldisorder, the presence of the disorder in a yet asymptomatic(pre-symptomatic) subject, the level of severity of the disorder in asymptomatic subject, and what therapeutic measures should be taken toprevent or treat the pre-symptomatic or symptomatic subject.

For example, the data can be used in a method of predicting the risk ofthe development of a neurological disorder in a mammalian subject. Themethod comprises collecting data about the subject from a number ofdifferent sources, including: screening for the presence of a biomarker(indicative of the neurological disorder); performing diagnostic imagingof the subject; performing behavioral tests indicative of theneurological disorder; measuring the subject's exposure to anenvironmental factor; measuring/identifying a physical characteristic ofthe subject; and determining the family history of the subject for theneurological disorder, the combined data obtained from these activitiesbeing indicative of the presence of, or risk of developing, thedisorder.

The data collected from the above-described sources can be organized bysubject or pool of subjects, or by data source. In addition, this datacan be obtained from a single time point or multiple time points.

For example, latitudinal data is data collected from one subject usingdifferent sources (i.e., different types of testing, as described indetail below) at one time point. Latitudinal data from the same source(I.e., same type of testing) can also be obtained from multiplesubjects. Subjects are asymptomatic or symptomatic. Asymptomaticsubjects are either control subjects who never develop the disorder, orare pre-symptomatic subjects who will develop the disorder at a latertime.

Longitudinal data is data collected from the same subject usingdifferent sources obtained at different time points. For example, If thesubject is asymptomatic when the first collection of data is obtained,this collection time point will be point 0 (longitudinal). Data will becollected at later time points from the same source(s). Data frommultiple subjects obtained from a single source (same type of testing)can be tracked longitudinally as well.

B. Neurological Disorders

The neurological disorders to be tracked can be any disorder whichaffects the function of central nervous system (CNS) including the brainand/or spinal cord, of a mammalian subject. The mammalian subject can beany mammal, including a human, bovine, equine, canine, porcine, feline,murine, rattine, lepine, hircumine, cervine, ovine, etc. The subject canbe asymptomatic or having symptoms of a neurological disorder.

For example, such a disorder can be a neurodegenerative disorderresulting from the degeneration of a component of the nervous system.The disorder may be a neurotrauma disorder resulting from a physicalinjury. In addition, the disorder may be a neuropsychology disorderwhich includes any disorder or injury resulting in an atypicalpsychological characteristic or behavior, and thus neuropsychologydisorders may also be neurodegenerative and/or neurotrauma disorders.The disorder can have any etiology, including, but not limited to, abacterial, fungal, viral, or prionic infection, a genetic mutation orpolymorphism, traumatic injury, or exposure to any number ofenvironmental toxins, irradiation, etc.

The neurodegenerative disorder can be a polyglutamine (PolyQ) disease,such as, but not limited to, pinocerebellar ataxia type 1 (SCA1), SCA2(Spinocerebellar ataxia Type 2), SCA3 (Spinocerebellar ataxia Type 3 orMachado-Joseph disease), SCA6 (Spinocerebellar ataxia Type 6), SCA7(Spinocerebellar ataxia Type 7), SCA17 (Spinocerebellar ataxia Type 17),DRPLA (Dentatorubropallidoluysian atrophy), HD (Huntington's disease),SBMA (Spinobulbar muscular atrophy or Kennedy disease), dentatorubralatrophy or pallidoluysian atrophy. Alternatively, the disorder can be anon-polyglutamine disease, such as, but not limited to, disease is FRAXA(Fragile X syndrome), FXTAS (Fragile X-associated tremor/ataxiasyndrome), FRAXE (Fragile XE mental retardation), FRDA (Friedreich'sataxia), DM (Myotonic dystrophy), SCA8 (Spinocerebellar ataxia Type 8),or SCA12 (Spinocerebellar ataxia Type 12. The disorder may instead beAlzheimer's disease, multiple sclerosis (MS), amyotrophic lateralsclerosis (ALS), spinocerebellar ataxias, trinucleotide repeat disorder,dementia, multiple system atrophy, HIV-associated neurocognitivedisorders (HAND), or polyneuropathy, hearing loss, ataxia, retinitispigmentosa, and cataract (PHARC), Parkinson's disease, essential tremor,cerebellar tremor, dystonic tremor, orthostatic tremor, Parkinsoniantremor, rubral tremor, or psychogenic tremor.

The neuropsychology disorder may be Arachnoid cysts, Arachnoiditis,Asperger's Syndrome, Ataxia Telangiectasia, Arteriovenous Malformations,Attention Deficit/Hyperactivity Disorder, Autism Barth Syndrome, BattenDisease, Behcet's Disease, Bell's Palsy Bernhardt-Roth Syndrome,Binswanger's Disease, Blepharospasm Bloch-Sulzberger Syndrome,Brown-Sequard Syndrome, CADASIL Canavan Disease, Capgrass Syndrome,Causalgia Central Cord Syndrome, Central Pain Syndrome, Central PontineMyelinosis, Cerebellar Hypoplasia, Cerebral Anoxia/Hypoxia, CerebralArteriosclerosis, Cerebral Cavernous Malformations, Cerebral Palsy,Cerebro-Oculo-Facio-Skeletal Syndrome, Charcot-Marie-Tooth Disease,Chiari Malformations, Childhood Anxiety Disorders, ChildhoodDisintegrative Disorder, Childhood Mood Disorders, Cholesteryl-EsterStorage Disease, Chronic Inflammatory Demyelinating Polyneuropathy,Chronic Pain Syndrome, Chung-Strauss Syndrome, Cluster HeadachesCoffin-Lowry Syndrome, Colprocephaly Coma & Persisting Vegetative StateConduct Disorder, Oppositional Defiant Disorder, Congenital MyastheniaCongenital Myopathy, Corticobasal Degeneration, Craniosynostosis,Creutzfeld-Jakob Disease, Cushing's Disease, CVAs, CytomegalovirusDandy-Walker Syndrome, Dawson Disease, De Morsier's Syndrome,Dejerne-Klumpke Palsy Delirium, Dementia Pugilistica, Dermato-myositis,Devic's Disease, Diabetic Neuropathy, Disconnect Syndromes, Disorders ofWritten Language, Down Syndrome, Dravet Syndrome, Dysautonomia,Dyssynergia, Cerebellaris Myoclonica Dystonias, Empty Sella Syndrome,Encephalitis, Encephalopathy, Encephaloceles, Epilepsy, Erb-DuchennePalsy, Fabry Disease, Fahr's Syndrome, Familial Periodic Paralyses,Familial Spastic Paraplegia, Farber's Disease, Fatal Familial Insomnia,Febrile Seizures, Fibromuscular Dysplasia, Fibromyalgia, Fragile XSyndrome, Friedeich's Ataxia, Frontotemporal Dementia, Gaucher Disease,Gerstmann-Straussler-Scheinker Disease, Gerstmann Syndrome,Glossopharyngeal neuralgia, Guillain-Barre, Hallervorden-Spatz Disease,Hemicrania, Continua Hemifacial Spasm, Hereditary Spastic Paraplegia,Herpes Zoster Oticus, HIV/AIDS, HIV/AIDS Dementia Complex, Holmes-AdieSyndrome, Holoprosencephaly, Homocystinurua, Hughes Syndrome,Huntington's Disease, Hydramyelia, Hydranencephaly, Hydrocephalus,Hydromyelia, Hypersomnia, Hypertonia, Hypotonia Increased IntracranialPressure, Infantile Hypotonia, Infantile Neuroaxonal Dystrophy,Infantile Refsum Disease, Infantile Spasms/West Syndrome, Iniencephaly,Intrauterine Teratogen Exposure, Isaac's Syndrome, Joubert Syndrome,Kawasaki Disease, Kearns-Sayre Syndrome, Kennedy's Disease, KinsbourneSyndrome, Kleine-Levin Syndrome, Klinefelter Syndrome, Klippel-FeilSyndrome, Klippel-Trenaunay Syndrome, Kluver-Bucy Syndrome, Kra bbeDisease, Kuru Lambert-Eaton Myasthenia Syndrome, Landau-KleffnerSyndrome, Lead Poisoning Leigh's Disease, Lennox-Gastaut Syndrome,Lesch-Nyhan Syndrome, Lewy-Body Dementia, Lipoid ProteinosisLissencephaly, Locked-in Syndrome, Lyme Disease, Machado-Joseph Disease,Macrencephaly, Maple Syrup Urine Disease, Mathematics Disorders MeakesDisease, Meningitis, Microcephaly, Migraine, Mitochondrialcardiomyopathies, Mitochondrial Myopathies, Megalencephaly,Melkersson-Rosenthal Syndrome, Mental Retardation, MetachromaticLeukodystrophy, Miller-Fisher Syndromes, Mobius Syndrome, MonomelicAmyotrophy, Motor Neuron Diseases, Moyamoya Disease,Mucopolysaccharidosis, Multifocal Motor Neuropathy, Multi-InfarctDementia. Multiple Sclerosis, Multi-System Atrophy with OrthostaticHypotension, Multi-System Atrophy without Orthostatic Hypotension,Muscular Dystrophy, Myasthenia Gravis, Myoclonus Myopathy, Myotonia,congenital Narcolepsy, Neuroacanthocytosis, Neurofibromatosis.Neuroleptic Malignant Syndrome, Neuronal Ceroid Lipofuscinoses,Neurosarcoidosis, Neurosyphilis, Neurotoxicity, Niemann-Pick Disease,Nonverbal Learning Disability, Normal Pressure Hydrocephalus OccipitalNeuralgia, Ohtahara Syndrome, Olivopontocerebellar Atrophy, OpsoclonusMyoclonus, Orthostatic Hypotension, Paraneolastic Syndromes,Parasthesias, Parkinson's Disease, Paroxysmal Choreoathetosis,Paroxysmal Hemicrania, Parry-Romberg, Pelizaeus-Merzbacher Disease,Periarteritis Nodosa, Peripheral Neuropathy, PeriventricularLeukomalacia, Pick's Disease, Piriformis Syndrome, PKU PolymyositisPompe Disease, Porencephaly, Postural Tachycardia, Prader Willi, PrimaryLateral Sclerosis, Primary Progressive Aphasia, Progressive MultifocalLeukoencephalopathy, Progressive Supranuclear Palsy, PseudotumorCerebri, Psychotic Disorders, Rasmussen's Encephalitis, ReadingDisorders, Repetitive Motor Disorders, Restless Leg Syndrome, RettSyndrome, Reye's Syndrome, Rheumatoid Arthritis, Sandhoff Disease,Schilder's Disease, Schizencephaly, Sclerodoma, Semantic Dementia,Septo-Optic Dysplasia, Shaken Baby Syndrome, Shingles, Sjogren'sSyndrome, Sleep Apnea, Somatoform and Conversion Disorders, SotosSyndrome, Spina Bifida, Spinal Cord Injuries, Spinal Muscular Atrophy,Spinocerebellar degeneration, Stiff-Person Syndrome, StriatonigralDegeneration, Sturge-Weber Syndrome, Subcortical-Vascular Dementia,SUNCT Headaches, Sydenham Chorea, Syncope Syringomyelia, Systemic Lupus,Tabes Dorsalis, Takayasu's Disease, Tardive Dyskinesia, Tarlov Cysts,Tay-Sachs Disease, Tethered Spinal Cord Syndrome, TIAs Thoracic OutletSyndrome, Thyrotosic Myopathy, Todd's Paralysis, Tourette's Disorder,other Tic Disorders, Transverse Myelitis, Traumatic Brain Injury,Trigeminal Neuralgia, Tropical Spastic Paraparesis, Troyer Syndrome,Tuberous Sclerosis, Turner Syndrome, Vasculitis, von Economo's Disease,von Hippel-Lindau Disease, Wallenberg Syndrome, Wegener'sgranulonoatosis, Wernicke-Korsakoff Syndrome, Whiplash, Whipple'sDisease, Williams Syndrome, Wilson's Disease, Wolman's Disease, and/orZellweger Syndrome.

C. Relational Neurological Databases, Systems and Uses

The above information and data is included in a neurobiological analysissystem. Such a system can further include, for example, a processor aswell as memory, the memory used to store and retrieve data records whilethe processor applies instructions from an algorithm. Speed,scalability, memory and processing power, and communication bandwidthare significant to the efficient operation of the system. The systemshould be capable of managing and processing multiple large data filesin parallel. Data sets include at least one or more data files, forexample, biomarker screening data, diagnostic imaging data, behavioraltest data, exposure to environmental risk factors, subject health data,and family medical history data. Thus the processor must be capable ofreading the data sets, executing the algorithm and deriving the solutionin the form of a subject data-specific file, i.e., the “risk profile”therefrom. Such systems that are known in the art and are suitable foruse in accordance with the disclosure include those running Intel orMotorola processors, as well as larger supercomputer systems such asCray XC30 and similar systems.

In one aspect, the system of the present disclosure includes arelational database. A relational database is provided with a pluralityof data entries stored in the database. The data entries have ahierarchical relationship. The data entries are assigned an itemidentifier that uniquely identifies each of the data items. A multipledigit outline number is assigned to each of the data entries wherein thedigits of the outline number correspond to the hierarchical levels ofthe data entries. A hierarchical level identifier is assigned to each ofthe data entries wherein the hierarchical level identifier equal thenumber of non-zero digits in the outline number. An organizational tableis created wherein the table includes a row for each of the data entriesand the table includes multiple columns. The columns comprise a columnfor the item identifier, a plurality of outline number columns whereeach outline number column comprises one digit of the outline numbersuch that each of the digits is stored in a separate column, and ahierarchical level column comprising the level identifier.

Similarly, U.S. Pat. No. 8,280,750 provides examples of a useful patientdata mining system and method. Herein, such a system and method forscreening for neurological disease or injury is provided. The methodincludes the steps of retrieving a test for assessing risk ofneurological disorder, the test including a plurality of data fieldsrelating to neurological disease or injury risk factors; accessing adatabase to populate the data fields with information of an individualpatient; and calculating a risk assessment of the individual patientdeveloping neurological disease. A system includes a first databaseincluding a plurality of structured computerized patient records; asecond database including a knowledge base relating to neurologicaldisease, the second database including at least one test for determiningneurological disease risk; and a processor for retrieving the at leastone test from the second database, populating the at least one test withpatient information retrieved from the first database and calculating arisk assessment for at least one patient. The method may further includeretrieving first medical data associated with one patient from adatabase, identifying a probability that a medical device or drug ortreatment regimen provides a medical benefit to the one patient thatexceeds a cost associated with providing the medical device etc. to theone patient with reference to the first medical data and a probabilisticmodel, the probabilistic model having a plurality of model parameters,each model parameter corresponding to one type of datum in the firstmedical data, and providing the medical device etc. to the one patientin response to the identified probability exceeding a firstpredetermined threshold. See, for example, U.S. Publication No.2013/0124224.

The present system built in accordance with the applications describedherein is based on data mining and knowledge discovery technology. Thesystem contains data mining models stored in the database. The modelsuse the classification approach algorithms of data mining technology tomeasure and predict the survivability of patients based on the medicalrecords of the patients. The models are designed to predict thepercentage of survivability years after the time of diagnosis. Thesystem contains novel user-friendly interfaces, which allow the user toregister the medical variables of the patients so monitored, forexample, to predict responses to treatments, suggest early intervention,and generate survivability and quality of life reports and predictions.The system also contains a set of functionality that allows the systemadministrators to control and monitor the contents of the database, andgate receipt of information, for example, in accordance with HIPAA andclinical requirements, or for subscription purposes. See, for example,U.S. Publication No. 2013/0173282.

In many countries, health and patient privacy laws requirede-identification of certain medical records. This is accomplished bymeans known in the art. For example, WO 2006015100 describes a methodfor linking de-identified patients using encrypted and unencrypteddemographic and healthcare information from multiple data sources. Asapplied herein, the present system includes a longitudinal database ofde-identified patient healthcare transaction data records linked bylongitudinal linking tags (IDs). A new healthcare transaction datarecord, which may include alphanumeric identification code attributes,third party attributes and/or demographic attributes, is assigned anlinking ID associated with a previous healthcare transaction data recordbased upon successful comparison of either a designated set ofidentification code attributes or a designated set of demographicattributes. The longitudinal data base is assembled by a matchingprocess in which a new data record is compared level by level withprevious healthcare transaction data records through a hierarchy of afirst series of matching levels each defined by a designated set ofalphanumeric identification code attributes and a second series ofmatching levels each defined by a designated set of attributes includingdemographic attributes and then assigned the ID associated with asuccessfully matched reference data record.

The system is useful for identifying groups of patients with similarphysiological characteristics and risk profiles. The present system alsoprovides for partitioning a plurality of patients into risk profilegroups. See, for example, U.S. Publication No. 2010/0016743. Such asystem is useful for determining patient treatment response outcomes.See, for example, U.S. Pat. No. 8,655,817.

A medical digital expert system is provided, to predict a patient'sresponse to a variety of treatments (using pre-treatment information).The system utilizes data fusion, advanced signal/information processingand machine learning/inference methodologies and technologies tointegrate and explore diverse sets of attributes, parameters andinformation that are available to select the optimal treatment choicefor an individual or for a subset of individuals suffering from anyillness or disease including psychiatric, mental or neurologicaldisorders and illnesses. The methodology and system can also be used todetermine or confirm medical diagnosis, estimate the level, index,severity or critical medical parameters of the illness or condition, orprovide a list of likely diagnoses for an individualsuffering/experiencing any illness, disorder or condition.

The systems and methods of the present application includes embodimentsthat allow users to more easily and efficiently compare medical data inan automated, computerized system using a variety of visualizationtools, by operation on datasets sourced from a variety of entities. See,for example, U.S. Publication No. 2014/0022255.

The system is adaptable for integration into physician work systems. Forexample, electronic patient records and documentation are usable withpush and pull of the data to and from the database. See, for example,U.S. Pat. No. 8,566,123. Medical records software interfaces in thesystem, allow a clinician, e.g., nurse or doctor, to combine entry ofnew patient orders, prescriptions, flowsheet observations, etc. in theirworkflow to interface and pull patient data from a database into theirdisplay. The user can select on their user interface one or morecategories of a patient record, e.g., significant events, scans, testsor orders, and view or edit prior entries in the database in thesecategories, and add additional documentation. The documentation iswritten or pushed to defined areas of the database, one devoted topatient documentation and a second area corresponding to the selectedcategory, e.g., orders. The method and apparatus improves workflowefficiency and promotes a smooth transition from the thought process ofthe clinician to the ordering or prescription process, without the needfor changing venues or screen displays.

The system of the present disclosure may be executed and or delivered inwhole or in part, via networks. For example, an Internet system forconnecting healthcare providers and patients is described in U.S.Publication No. 2012/0284045. Networked systems provide for remote-basedmonitoring and patient feedback systems, which are incorporated into thedatabase. See, for example, U.S. Publication No. 2014/0052464, whichdetails a method and system for remote patient monitoring. A system forremotely monitoring a patient includes a plurality of input sourcesoperable to acquire information corresponding to a well-being conditionof a patient, an external database for storing analytical models andmedical data, and a central control system being operable to receive thesignals from the input sources, and execute an algorithm to select ananalytical model from the database based on the (real-time) informationand the data, analyze the information and the data with the parametersof a medical model to determine a state of the patient and formulate ahealth prediction, determine a recommendation as a result of the stateand the health prediction, and transmit the recommendation to at leastone external entity for providing support and assistance to the patientor to a caregiver of the patient.

An Internet-based system involves a database and search capabilities forconnecting patients with healthcare providers, e.g., physicians,hospitals, nursing homes, treatment facilities, etc., clinical sites,etc., and further enables such providers to reach patients with whomthey may not otherwise come into contact. A patient may access thehealthcare provider information through a search conducted using asearch engine, such as Google, Yahoo, etc. Alternatively, a patient mayaccess the company Web site's predetermined Web page that providessearch capabilities on its database. A patient may research a healthcareprovider based on criteria specified by the patient. Informationprovided to the patient may be in the form of a report, profile,ratings, etc., including patient-provided information,physician-verified information, and information verified by anindependent third party. The verified information and ratings providedby the Web site enable patients to differentiate among healthcareproviders and thereby select the provider that best meets theirindividual needs.

Such systems of the present disclosure may further include integratedelectronic patient health care and billing coordination systems, see,for example, U.S. Pat. No. 8,615,413. A patient care coordination systemcan include a plurality of hand-held computers in communication with acloud computing network or a remote server that has an accessibledatabase of all patients and the health care information of each. Thecloud computing network or remote server synchronizes, in real time,patient health care information input in any one of the plurality ofhand-held computers with all the others of the plurality of hand-heldcomputers. The hand-held computers are able to download and view thepatient health care information in the database in a user-friendlygraphic user interface equipped with a touch screen for ease of userdata navigation. The cloud computing network or remote server alsoreceives, as input, data from patient care devices that are used tomonitor patient condition periodically or continuously and store thesein the database for the appropriate patient. In addition, the cloudcomputing network or remote server transmits encrypted electronicdigital patient health care information to a third party and receivesacknowledgment of third party receipt of the information. The cloudcomputing network or remote server monitors fee-bearing informationexchanged with the third party and automatically assesses apredetermined fee based on fee-bearing information exchanged and storesthe billing information to the appropriate patient in the data base.

D. Application of Systems to Clinical Trials

In yet another aspect, the disclosure provides a system and method thatidentifies patients for clinical drug or device trials. See, forexample, U.S. Publication No. 2007/0106531. Such a system rapidly andprecisely identifies patient candidates for clinical trials. It includesa database component operative to maintain a hospital patient databasecomponent and its plurality of hospital databases and theircorresponding plurality of patient names and medical records, incommunication with one or more medical practice database components andtheir corresponding plurality of specialties and their correspondingplurality of patient names and medical records. The method and systemalso include a clinical studies database component and its correspondingplurality of clinical studies, a communications component to receivechanges to said database component, and a processor programmed toperiodically match compatible patients and clinical studies, and togenerate reports to medical practices in said medical practice databasehaving matched patients. The processor may be programmed to search freetext keywords and phrases.

In another aspect, the system analyzes patients with respect to clinicalmedical trials, see, for further example, U.S. Pat. No. 7,401,028. Asystem of the present disclosure provides for various methods forevaluating one or more patients for their potential in a medical study,and includes: a database component operative to maintain a medicalpractice database component and a clinical studies database component.The system further includes a component to observe changes to thedatabase components, and a communications component to provide an alert,for example, to the patient, or a medical practitioner. The system alsoincludes a processor programmed to update the database components,periodically match compatible medical specialties with the medicalstudies, and generate reports of the matched medical practices in themedical practice database. The system may also include a fee databasecomponent, which the processor uses to calculate a fee for conductingthe study.

E. Algorithms

The processor reads the data sets exemplified above, executing analgorithm and deriving the solution in the form of a subjectdata-specific file for display, or executing an outcome as desired. Theparticular algorithm employed may vary across particular applications.However, the objective in most instances where data is manipulated, onedesirable outcome is generation of a risk-score or similarly predictivemodel based on the aggregation and weighting of data from relevant datasets, using common statistical tools and models. For example, detectionof gene polymorphisms can be predictive of risk for disease, such aswith Apolipoprotein E (ApoE) a polymorphic apolipoprotein, with threemajor isoforms: ApoE2, ApoE3 and ApoE4. ApoE4 is found in approximately14 percent of the population, and has been implicated inatherosclerosis, Alzheimer's disease, impaired cognitive function,reduced hippocampal volume, faster disease progression in multiplesclerosis, unfavorable outcome after traumatic brain injury, ischemiccerebrovascular disease, sleep apnea, and reduced neurite outgrowth.Subjects that are homozygous for ApoE4 have a different risk profilethan heterozygous subjects. However, high-quality, high-relevancysubject specific data is useful if it reflects an integration of variousrisk profiles for different types of data, when such data and risk isexamined and evaluated in the aggregate. For example, the detection ofApoE4 in a subject with traumatic brain injury is informative, butimaging of the injury site and the healing process (e.g., longitudinalmonitoring) provides a more refined analysis of the medical condition,and better prognosis. Similarly, multiple biomarker studies such asthose from Genome Wide Association Studies (GWAS) provide moreinformation than single biomarker tests. See, for example, Mailer etal., Nature Genet. (2006) 38:1055-1059, which shows that commonvariation in three genes, including a noncoding variant in CFH, stronglyinfluences risk of age-related macular degeneration. Additional usefulinformation will be apparent to medical professionals, and such data areconsidered in the aggregate to determine an outcome's probability andthe possible best courses of treatment or prevention given statisticsthat are highly patient-relevant.

The algorithms useful herein are based on computational biology,bioinformatics and mathematical biology, which draw from mathematics andinformation science. They employ application of data-analytical andtheoretical methods, mathematical modeling and computational simulationtechniques to the study of biological and behavioral neuroscience. Theirobjective is application of computational tools and approaches forexpanding the use of biological, medical, behavioral or health data,including those to acquire, store, organize, archive, analyze, orvisualize such data. The derivation of an algorithm to parse individualdata sets and quantitate an aggregate a value for their application, andthe translation of such to machine-readable format is considered to bewithin the means of one of ordinary skill in the various arts, in viewof such arts and the teachings provided herein.

Computational neuroscience is a rapidly evolving field, and is focusedon the study of brain function in terms of the information processingproperties of the structures that make up the nervous system. It modelsthe brain in order to examine specific aspects of the CNS. Various typesof models of the brain exist (see, e.g., Sejnowski, et al. (1988)Computa. Neurosci. 4871:241). These include realistic brain models andsimplifying brain models. Realistic models look to represent everyaspect of the brain, including as much detail at the cellular level aspossible. Realistic models provide the most information about the brain,but also have the largest margin for error. Variables in a brain modelcreate the possibility for additional error, and the models are furtherlimited by knowledge of cellular structures. Realistic brain models arecomputationally intensive. Simplifying brain models look to limit thescope of a model in order to assess a specific physical property of theneurological system. This allows for less intensive computationalresources for problems to be solved. The disclosure provides forimproved algorithms and data structures relative to the ones currentlyused to increase the speed in calculating solutions to particularqueries.

Likewise, computational pharmacology is essential for analyzing drugdata, employing computational methods to analyze massive data sets. Thisallows for better evaluation of data and provides for more accurateclinical development of drugs. The models and algorithms ofcomputational pharmacology are useful in applications whereby clinicaland experimental studies are considered or undertaken. Particular andnon-limiting examples include deriving exclusion criteria for a clinicalstudy, and in real-time matching the suitability of a particular subjectfor a clinical study based on genetic and other biomarker information,scans, personal and family medical history.

Computational biomodeling is a field concerned with building computermodels of complex biological systems. Computational biomodeling aims todevelop and use visual simulations in order to assess the complexity ofbiological systems. This is accomplished through the use of specializedalgorithms, and visualization software. These models allow forprediction of how systems will react under different environments.Computational biomodeling is of particular relevance to neuroscience, asthe brain and nervous system are good examples of complex systems.Particular and non-limiting examples may include olfactory function testdata as a predictor of Parkinson's risk, whereby modeling of olfactoryfunction and function of dopamine-generating cells in the substantianigra, a region of the midbrain, are monitored in parallel, over a timeinterval (e.g., a five-year longitudinal study).

F. Connectivity and Communications

In many embodiments, it is desirable that the above-described system bemodular, but capable of rapid, or even real-time information exchange. Auseful and non-limiting example is a system that stores data at multiplesites across a network (e.g., patient data at a physician's office;medical images and test results at a hospital and/or outpatient center;genetic frequencies and correlations at various Internet-accessiblesites) and where the algorithm runs on a connected server or servers,rather than on a local computing device such as a desktop, laptop, PDA,tablet or smartphone. This is often described as “in the cloud,” where acomputing hardware machine or group of computing hardware machinescommonly referred as a server or servers is connected through acommunication network such as the Internet, an intranet, a local areanetwork (LAN) or wide area network (WAN). Connectivity of variousmodules in and to the system will depend of the device. These areexemplified by: optical networks, ITU-T G.hn (1 Gigabit/s) local areanetworks or IEEE 802.11 (WiFi) networks.

Any individual user who has permission to access the server can use theserver's processing power to run an application, store data, or performany other computing task. Infrastructure as a service (IaaS), Platformas a service (PaaS) and Software as a service (SaaS) models are allapplicable to the systems described herein.

G. Provision of Neuroanalytical Services

In accordance with the above system, data and methods, by way of furtherextrapolation and example, any individual user who has permission toaccess the system server can use the server's processing power to run anapplication, store/retrieve data, or perform any other computing tasklinking a plurality of datasets (e.g., subject-independentneuroanalytical data sets and subject-dependent neuroanalytical datasets). Users may receive or send content and data from smartphones,tablets, laptops, or dedicated devices (e.g., monitors). Delivery ofinformation is real-time, or with minimal delays due to networks andprocessing delays. Infrastructure as a service (IaaS), Platform as aservice (PaaS) and Software as a service (SaaS) models are allapplicable to the systems described herein. In certain currently usefulembodiments, two combinations and even all three models operatesimultaneously.

SaaS delivers business processes and applications, as standardizedcapabilities for a usage-based cost at an agreed, business-relevantservice level. All infrastructure and IT operational functions areabstracted away from the consumer. SaaS models are useful, for example,in applications that are used in the clinic.

PaaS delivers application execution services, for applications writtenfor a pre-specified development framework. Service levels andoperational risks are shared because the consumer must takeresponsibility for the stability, architectural compliance, and overalloperations of the application while the provider delivers the platformcapability (including the infrastructure and operational functions) at apredictable service level and cost. PaaS models are useful, for example,in applications that involve hospitals or imaging centers.

IaaS abstracts hardware into a pool of computing, storage, andconnectivity capabilities that are delivered as services for ausage-based (metered) cost. Its goal is to provide a flexible, standard,and virtualized operating environment that can become a foundation forPaaS and SaaS. IaaS is usually seen to provide a standardized virtualserver. The consumer takes responsibility for configuration andoperations of the guest Operating System (OS), software, and Database(DB). Compute capabilities (such as performance, bandwidth, and storageaccess) are also standardized. Service levels cover the performance andavailability of the virtualized infrastructure. The consumer takes onthe operational risk that exists above the infrastructure. IaaS modelsare useful, for example, in applications that involve clinical trials.

In addition, various configurations are applicable. Public Cloud is apool of computing services delivered over the Internet, with variationsbeing Shared Public Cloud and Dedicated Public Cloud. The DedicatedPublic Cloud provides functionality similar to a Shared Public Cloudexcept that it is delivered on a dedicated physical infrastructure.Private Cloud is a pool of computing resources delivered as astandardized set of services that are specified, architected, andcontrolled by a particular enterprise, driven by the need to maintaincontrol of the service delivery environ-ment because of applicationmaturity, performance requirements, industry or government regulatorycontrols, or business differentiation reasons. Private Cloud may beSelf-hosted Private Cloud, Hosted Private Cloud or Private CloudAppliance. The particular classifications used to describe thedisclosure will depend on the relationship between modules of the systemused for particular information. For example, where the system isdesigned to access public data and compare subject information thereto,as to public biomarker data (such as Gen bank) the disclosure will havePublic Cloud features; and for certain aspects of subject data (such assubject genetic information/SNPs) the disclosure will have Private Cloudfeatures. The architecture of the system will be apparent to one ofskill in the information technology arts, in view of the teachingsherein.

Equivalents

Those skilled in the art will recognize, or be able to ascertain, usingno more than routine experimentation, numerous equivalents to thespecific composition and procedures described herein. In particular, theabove examples provide certain embodiments of the disclosure and are notintended to be limiting. The modular structure of the system and use ofthe data sets in various combinations for achieving different outcomesare considered to be within the means of one of ordinary skill in thevarious arts, in view of such arts and the teachings provided herein.Such equivalents are considered to be within the scope of thisdisclosure, and are covered by the following claims.

1. A method of predicting the risk of developing a neurological disorderin a first mammalian subject, comprising the steps of: screening for thepresence of one or more biomarkers; performing diagnostic imaging of thesubject; performing behavioral tests indicative of the neurologicaldisorder; measuring the subject's exposure to an environmental factor;measuring/identifying a physical characteristic of the subject;determining the presence of the neurological disorder in a family memberof the first subject; combining the results from the steps above; andcomparing the combined results with combined results obtained from asecond mammalian subject diagnosed with the neurological disorder, ahigh correlation between the combined results from the second subjectand the combined results obtained from the first subject beingindicative of a heightened risk of the first subject developing theneurological disorder.
 2. The method of claim 1, wherein the screeningstep comprises screening for a biomarker which is a nucleic acid,polypeptide, prion, virus, brain plaque, CNS plaque, fibril,intranuclear neuronal inclusions, and/or brain structure abnormality. 3.The method of claim 2, wherein the nucleic acid is a gene or codingportion thereof, SNP, mRNA, miRNA, pri-miRNA, or prepri-miRNA.
 4. Themethod of claim 3, wherein the nucleic acid is over-expressed miR-196a,miR-29a, or miR-330.
 5. The method of claim 3, wherein the nucleic acidis under-expressed miR-133b, miR-205, miR-34b/c, miR-9, miR-9*, ormiR-132.
 6. . The method of claim 3, wherein the nucleic acid is amutation in the Cu/Zn superoxide dismutase 1 (SOD1) gene, an unstablemicrosatellite repeat (insertion mutation) in a gene, HTT gene, androgenreceptor on the X chromosome, ATXN1, ATXN2, ATXN3, ATXN7, TBP, CACNA1A,mutation in C9orf72 (on chromosome 9), FMR1 (on the X-chromosome), AFF2(on the X-chromosome), FMR2 (on the X-chromosome), FXN or X25,(frataxin—reduced expression), DMPK, OSCA or SCA8, PPP2R2B or SCA12,α-synuclein, leucine-rich repeat kinase 2 (LRRK-2), glucocerebrosidase(GBA), ABHD12, SNCA, or LRRK2.
 7. The method of claim 2, wherein thepolypeptide is a surface marker, tau protein, beta amyloid,polyglutamate (peptide), alpha-synuclein, non-Abeta component (NAC),polyQ expansion, TDP-43 protein aggregate, FUS protein aggregate, ormutant Huntingtin aggregate.
 8. The method of claim 2, wherein thebiomarker is a Lewy body fibril, neurofibrillary tangle, oralpha-synuclein fibril.
 9. The method of claim 2, wherein the biomarkeris an amyloid plaque or a senile plaque.
 10. The method of claim 2,wherein the biomarker is Herpes simplex virus-1(HSV-1 type HHV-1),roseolovirus (type HHV-6), Epstein Barr virus (EBV type HHV-4),Varicella zoster virus (VZV type HHV3), H1N1 Influenza a viruses, HIV,and/or HTLV-II .
 11. The method of claim 1, wherein the screening stepis performed by obtaining a sample of a body fluid or tissue andscreening for the biomarker in the sample.
 12. The method of claim 11,wherein the body fluid is blood, cerebral spinal fluid, serum, lymph,saliva, lacrimal secretion, sweat, mucous, vaginal secretion, lymph,urine, or seminal fluid.
 13. The method of claim 1, wherein thediagnostic imaging performed is an x-ray, a computerized axialtomographic (CAT) scan, magnetic resonance imaging (MRI) scan,functional MRI (fMRI), single photon emission computed tomography(SPECT) perfusion image, computed tomography (CT) scan, proton MRspectroscopy scan, positron emission tomographic (PET) scan, and/or[F-18] fluoro-2-deoxy-D-glucose-positron emission tomographic (18F-FDGPET) scan, DaTSCAN, and/or ultrasound.
 14. The method of claim 1,wherein the behavioral test performed measures sensory abilities, motorfunctions, body weight, body temperature, and/or pain threshold,learning abilities, memory, and symptoms of anxiety, depression,schizophrenia, and/or drug addiction.
 15. The method of claim 14,wherein the behavioral test performed measures acoustic startle, eyeblink, pupil constriction, visual cliff, auditory threshold, and/orolfactory acuity.
 16. The method of claim 1, wherein the subject'sexposure to pesticides, herbicides, fungicides, solvents, other toxicchemicals, tobacco smoke, heavy metals, electromagnetic fields,ultraviolet radiation, and/or diet (malnutrition, vitamin deficiency),and/or alcohol consumption is measured.
 17. The method of claim 16,wherein the subject's exposure to iMPTP(1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine), rotenone, paraquat,maneb, Agent orange, manganese, lead, iron, methylmercury, copper, zinc,selenium, polychlorinated biphenyls, and/or a reactive oxygen species(ROS) is measured.
 18. The method of claim 1, wherein exposure of thesubject to the environmental factor causes apoptosis, oxidative stress,perturbed calcium homeostasis (loss of intracellular Ca⁺²),excitotoxicity, mitochondrial dysfunction, and/or activation ofcaspases.
 19. The method of claim 1, wherein the physical factormeasured is age, gender, ethnicity, heart rate, REM, cardioelectricalsignals, and/or the presence of genetic polymorphisms, endocrineconditions, oxidative stress, inflammation, stroke, traumatic braininjury, hypertension, diabetes, head/CNS trauma, depression, infection,cancer, vitamin deficiency, and/or immune and/or metabolic conditions.20. The method of claim 1, wherein the neurological disorder is aneurodegenerative disorder, a neurotrauma disorder, and/or aneuropsychological disorder.
 21. The method of claim 20, wherein theneurodegenerative disorder is a polyglutamine (PolyQ) disease,non-polyglutamine disease, Alzheimer's disease, multiple sclerosis (MS),amyotrophic lateral sclerosis (ALS), spinocerebellar ataxias,trinucleotide repeat disorder, dementia, multiple system atrophy,HIV-associated neurocognitive disorders (HAND), or polyneuropathy,hearing loss, ataxia, retinitis pigmentosa, and cataract (PHARC),Parkinson's disease, essential tremor, cerebellar tremor, dystonictremor, orthostatic tremor, Parkinsonian tremor, rubral tremor, orpsychogenic tremor.
 22. The method of claim 21, wherein thepolyglutamine disease is Spinocerebellar ataxia type 1 (SCA1), SCA2(Spinocerebellar ataxia Type 2), SCA3 (Spinocerebellar ataxia Type 3 orMachado-Joseph disease), SCA6 (Spinocerebellar ataxia Type 6), SCA7(Spinocerebellar ataxia Type 7), SCA17 (Spinocerebellar ataxia Type 17),DRPLA (Dentatorubropallidoluysian atrophy), HD (Huntington's disease),SBMA (Spinobulbar muscular atrophy or Kennedy disease), dentatorubralatrophy, or pallidoluysian atrophy.
 23. The method of claim 20, whereinthe neurodegenerative disorder is a non-polyglutamine disease.
 24. Themethod of claim 23, wherein the non-polyglutamine disease is FRAXA(Fragile X syndrome), FXTAS (Fragile X-associated tremor/ataxiasyndrome), FRAXE (Fragile XE mental retardation), FRDA (Friedreich'sataxia), DM (Myotonic dystrophy), SCA8 (Spinocerebellar ataxia Type 8),or SCA12 (Spinocerebellar ataxia Type
 12. 25. The method of claim 20,wherein the neurotrauma disorder results from a traumatic brain injury,concussion, or stroke.
 26. The method of claim 1, wherein the subject isasymptomatic.
 27. The method of claim 1, wherein the combination stepscomprise generating a risk score, and wherein if the risk score of thefirst subject is similar to the risk score of the second subject, thefirst subject has a heightened risk of developing the neurologicaldisorder.
 28. The method of claim 1, further comprising developing andimplementing a treatment plan to the first subject, the treatment plancomprising administering a therapeutically effective composition to thefirst subject.
 29. A system comprising: a processor and a memory, thememory having a neurodiagnostic algorithm and a plurality of data sets,the data sets including: a) biomarker screening data; b) diagnosticimaging data; c) behavioral test data; d) exposure to environmental riskfactors; e) subject health data; and f) family medical history data, theprocessor being capable of reading the data sets, executing theneurodiagnostic algorithm, and deriving a risk score or profiletherefrom.
 30. The system of claim 29, further comprising a displaymeans for visualizing the risk score or profile.
 31. The system of claim29, wherein the system memory is at locations distant from theprocessor.
 32. A data subscription service accessing the data sets ofclaim 29, wherein the neurodiagnostic algorithm predicts treatmentoutcomes from the risk profile.
 33. The system of claim 29, wherein thesubject health data is obtained from an individual asymptomatic forneurological disorders.
 34. A relational database comprising a pluralityof subject-independent neurodiagnostic data sets comprising: a)biomarker screening data; b) diagnostic imaging data; c) behavioral testdata; d) environmental risk factor data, the one or moresubject-dependent neurodiagnostic data sets further including subjectmedical history and subject family medical histories.
 35. A datasubscription service accessing the database of claim
 34. 36. A method ofproviding neuroanalytical services, comprising: generating a patientprofile for neurological risk, comprising analyzing subject-independentneuroanalytical data sets and subject-dependent neuroanalytical datasets; and delivering the patient risk profile to an end user.
 37. Themethod of claim 36, wherein subject-independent neuroanalytical datasets include third-party data of neurological disease-relevantbiomarkers, neural imaging data, environmental risk factors forneurological disorders; and wherein subject-dependent neuroanalyticaldata sets include subject and family medical environmental andbehavioral data medical history data.
 38. The method of claim 36,wherein the patient risk profile comprises an aggregated risk ofindividual risk factors delineated or calculated from thesubject-independent neuroanalytical data sets and subject-dependentneuroanalytical data sets.
 39. The method of claim 38, wherein analgorithm is biased to value risk higher from the subject-dependentneuroanalytical data as compared to subject-independent neuroanalyticaldata.
 40. The method of claims 36-39, wherein the risk profile isdelivered to an end user using SaaS, PaaS, or laaS-based service models.41. The method of claim 40, wherein Private and Public Cloudarchitecture is employed.
 42. The method of claim 41, wherein the enduser obtains the risk profile on a tablet, smartphone, or portablecomputing device.
 43. The method of claim 42, wherein the delivery isreal-time or near real-time.